Application of supervised random forest paradigms based on optimization and post-hoc explanation in underground stope stability prediction
文献类型：期刊论文
| 作者 | Li, Chuanqi2; Mei, Xiancheng3; Zhang, Jiamin1 |
| 刊名 | APPLIED SOFT COMPUTING
 |
| 出版日期 | 2024-03-01 |
| 卷号 | 154页码:21 |
| 关键词 | Underground stope Supervised random forest Post -hoc explanation Meta -heuristic optimization |
| ISSN号 | 1568-4946 |
| DOI | 10.1016/j.asoc.2024.111388 |
| 英文摘要 | This study applies a supervised random forest (SRF) paradigm to predict the underground stope stability. To improve the model prediction accuracy, 400 actual stopes with three main features (the optimized stability number (N), the hydraulic radius (HR), and a rock mass quality factor (Q)) are used to train the model, and nine meta -heuristic (MH) optimization algorithms are utilized to select the optimal hyperparameters of the SRF model. The prediction results show that the tiki taka algorithm -based SRF (TTA-SRF) model obtains the most satisfactory classification performance, with the smallest variance in the overfitting evaluation (Precision of 0.0201, Recall of 0.0257, Specificity of 0.0061, Accuracy of 0.0125, and F1 -measure of 0.0246). Furthermore, four post -hoc explainable techniques (i.e., model -independent) including the feature importance (FI), the Shapley additive explanations (SHAP), the partial dependence plot (PDP), and the individual conditional expectation (ICE) are adopted to explain the best prediction model. The results of the model interpretation show that all features are essential for predicting the stope stability and they have an opposite tendency on the stability prediction of stable and caved stopes. In particular, the limits at which HR, Q, and N do not contribute to the stope stability prediction are 18 m, 40, and 100, respectively. The results of optimization designs for a specific case stope based on the visualization program showed that the suggestions given by the prediction model are desirable. In conclusion, a high-performance and strongly explanatory prediction model is proposed in this study to facilitate the refinement of stope stability assessment in underground space. |
| 资助项目 | Project of Key Laboratory of Water Grid Project and Regulation of Ministry of Water Resources[QTKS0034W23291] ; China Scholarship Council[202106370038] |
| WOS研究方向 | Computer Science |
| 语种 | 英语 |
| WOS记录号 | WOS:001196995000001 |
| 出版者 | ELSEVIER |
| 源URL | [http://119.78.100.198/handle/2S6PX9GI/40971]  |
| 专题 | 中科院武汉岩土力学所 |
| 通讯作者 | Li, Chuanqi |
| 作者单位 | 1.SINOPEC Res Inst Petr Engn, Beijing 100101, Peoples R China 2.Grenoble Alpes Univ, Lab 3SR, CNRS UMR 5521, F-38000 Grenoble, France 3.Chinese Acad Sci, Inst Rock & Soil Mech, State Key Lab Geomech & Geotech Engn, Wuhan 430071, Peoples R China |
| 推荐引用方式 GB/T 7714 | Li, Chuanqi,Mei, Xiancheng,Zhang, Jiamin. Application of supervised random forest paradigms based on optimization and post-hoc explanation in underground stope stability prediction[J]. APPLIED SOFT COMPUTING,2024,154:21. |
| APA | Li, Chuanqi,Mei, Xiancheng,&Zhang, Jiamin.(2024).Application of supervised random forest paradigms based on optimization and post-hoc explanation in underground stope stability prediction.APPLIED SOFT COMPUTING,154,21. |
| MLA | Li, Chuanqi,et al."Application of supervised random forest paradigms based on optimization and post-hoc explanation in underground stope stability prediction".APPLIED SOFT COMPUTING 154(2024):21. |
入库方式： OAI收割
来源：武汉岩土力学研究所
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